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. 2023 Nov 24;102(47):e36265.
doi: 10.1097/MD.0000000000036265.

Screening and identification of key biomarkers associated with amyotrophic lateral sclerosis and depression using bioinformatics

Ziyue Wang  1 , Hao Yang  1 , Yu Han  1 , Jing Teng  1 , Xinru Kong  1 , Xianghua Qi  2
Affiliations

Screening and identification of key biomarkers associated with amyotrophic lateral sclerosis and depression using bioinformatics

Ziyue Wang et al. Medicine (Baltimore). .

Abstract

This study aims to identify common molecular biomarkers between amyotrophic lateral sclerosis (ALS) and depression using bioinformatics methods, in order to provide potential targets and new ideas and methods for the diagnosis and treatment of these diseases. Microarray datasets GSE139384, GSE35978 and GSE87610 were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between ALS and depression were identified. After screening for overlapping DEGs, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Furthermore, a protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software, and hub genes were identified. Finally, a network between miRNAs and hub genes was constructed using the NetworkAnalyst tool, and possible key miRNAs were predicted. A total of 357 genes have been identified as common DEGs between ALS and depression. GO and KEGG enrichment analyses of the 357 DEGs showed that they were mainly involved in cytoplasmic translation. Further analysis of the PPI network using Cytoscape and MCODE plugins identified 6 hub genes, including mitochondrial ribosomal protein S12 (MRPS12), poly(rC) binding protein 1 (PARP1), SNRNP200, PCBP1, small G protein signaling modulator 1 (SGSM1), and DNA methyltransferase 1 (DNMT1). Five possible target miRNAs, including miR-221-5p, miR-21-5p, miR-100-5p, miR-30b-5p, and miR-615-3p, were predicted by constructing a miRNA-gene network. This study used bioinformatics techniques to explore the potential association between ALS and depression, and identified potential biomarkers. These biomarkers may provide new ideas and methods for the early diagnosis, treatment, and monitoring of ALS and depression.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
The flow chart analysis of the method.
Figure 2.
Figure 2.
The volcano plot of DEGs. (A), volcano plot of DEGs in GSE139384; (B), volcano plot of DEGs in GSE35978; (C), volcano plot of DEGs in GSE87610; (D), venn diagram of DEGs in GSE139384, GSE35978, and GSE87619 gene chips. DEGs = differential expression genes.
Figure 3.
Figure 3.
(A), GO and KEGG analysis results of overlapping DEGs; (B) GO and KEGG analysis results of DEGs in the most significant module. The size of the bubble indicates the enrichment score; colors indicate enrichment significance. DEGs = differential expression genes, GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes.
Figure 4.
Figure 4.
The PPI network of overlapping DEGs was constructed using STRING. DEGs = differential expression genes, PPI = protein–protein interactions.
Figure 5.
Figure 5.
The PPI network among overlapping DEGs. DEGs = differential expression genes, PPI = protein–protein interactions.
Figure 6.
Figure 6.
The most significant module was obtained from PPI network. PPI = protein–protein interactions.
Figure 7.
Figure 7.
The biological process analysis and visualization of hub genes were performed using the Cytoscape BiNGO.
Figure 8.
Figure 8.
Integrated miRNA-gene interaction networks for the 6 hub genes. Red circles represent 6 hub genes. Blue squares represent miRNA which has connectivity with hub genes.
Figure 9.
Figure 9.
Minimal integrated miRNA-gene interaction networks for the 6 hub genes.
See this image and copyright information in PMC

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