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Identification of Candidate Genes and Therapeutic Agents for Light Chain Amyloidosis Based on Bioinformatics Approach

Authors Bai W, Wang H, Bai H

Received 24 August 2019

Accepted for publication 3 December 2019

Published 3 January 2020 Volume 2019:12 Pages 387—396

DOI https://doi.org/10.2147/PGPM.S228574

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Martin H Bluth


Wenxiang Bai,1,2,* Honghua Wang,1,* Hua Bai1,3

1Comprehensive Cancer Center, Xiangshui People’s Hospital, Xiangshui 224600, People’s Republic of China; 2Department of Respiratory Medicine, Xiangshui People’s Hospital, Xiangshui, 224600, People’s Republic of China; 3Department of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Hua Bai
Comprehensive Cancer Center, Xiangshui People’s Hospital, Xiangshui 224600, People’s Republic of China
Email baihua92@126.com

Objective: Systemic amyloid light chain (AL) amyloidosis is a rare plasma cell disease. However, the regulatory mechanisms of AL amyloidosis have not been thoroughly uncovered, identification of candidate genes and therapeutic agents for this disease is crucial to provide novel insights into exploring the regulatory mechanisms underlying AL amyloidosis.
Methods: The gene expression profile of GSE73040, including 9 specimens from AL amyloidosis patients and 5 specimens from normal control, was downloaded from GEO datasets. Differentially expressed genes (DEGs) were sorted with regard to AL amyloidosis versus normal control group using Limma package. The gene enrichment analyses including GO and KEGG pathway were performed using DAVID website subsequently. Furthermore, the protein–protein interaction (PPI) network for DEGs was constructed by Cytoscape software and STRING database. DEGs were mapped to the connectivity map datasets to identify potential molecular agents of AL amyloidosis.
Results: A total of 1464 DEGs (727 up-regulated, 737 down-regulated) were identified in AL amyloidosis samples versus control samples, these dysregulated genes were associated with the dysfunction of ribosome biogenesis and immune response. PPI network and module analysis uncovered that several crucial genes were defined as candidate genes, including ITGAM, ITGB2, ITGAX, IMP3 and FBL. More importantly, we identified the small molecular agents (AT-9283, Ritonavir and PKC beta-inhibitor) as the potential drugs for AL amyloidosis.
Conclusion: Using bioinformatics approach, we have identified candidate genes and pathways in AL amyloidosis, which can extend our understanding of the cause and molecular mechanisms, and these crucial genes and pathways could act as biomarkers and therapeutic targets for AL amyloidosis.

Keywords: light chain amyloidosis, bioinformatics approach, differentially expressed genes, candidate genes, therapeutic agent

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