Screening and identification of key biomarkers of depression using bioinformatics

Kong, Xinru; Wang, Chuang; Wu, Qiao-Lan; Wang, Ziyue; Han, Yu; Teng, Jie; Qi, Xianghua

doi:10.1038/s41598-023-31413-1

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…Genes that met the criteria ( P < .05, |logFC| ≥ 0.1) were considered to be differentially expressed. [30,31] The DEGs obtained from the 3 datasets were visualized using bioinformatics websites to generate volcano plots. In addition, the Venn online platform, Draw Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/), was used to depict the overlap of DEGs between ALS and depression for subsequent analysis.…”

Section: Methodsmentioning

confidence: 99%

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

Wang,

Yang,

Han

et al. 2023

Medicine

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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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Section: Methodsmentioning

confidence: 99%

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

Wang,

Yang,

Han

et al. 2023

Medicine

Self Cite

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Electrochemical biosensors for depression: Diagnosis and therapeutic monitoring

Asadi,

Ferdosi,

Anoosheh

et al. 2025

Clinica Chimica Acta

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Employing single-cell RNA sequencing coupled with an array of bioinformatics approaches to ascertain the shared genetic characteristics between osteoporosis and obesity

Liu,

Cao,

Liu

et al. 2024

Bone Joint Res

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AimsThis study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously.MethodsUsing weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.ResultsWGCNA revealed critical gene modules for OB and OP, identifying the Toll-like receptor (TLR) signalling pathway as a common factor. TLR2 was the most significant gene, with a pronounced expression in macrophages. Elevated TLR2 expression correlated with increased adipose accumulation, inflammation, and osteoclast differentiation, linking it to OP development.ConclusionOur study underscores the pivotal role of TLR2 in connecting OP and OB. It highlights the influence of TLR2 in macrophages, driving both diseases through a pro-inflammatory mechanism. These insights propose TLR2 as a potential dual therapeutic target for treating OP and OB.Cite this article: Bone Joint Res 2024;13(10):573–587.

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Screening and identification of key biomarkers of depression using bioinformatics

Cited by 3 publications

References 69 publications

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

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

Electrochemical biosensors for depression: Diagnosis and therapeutic monitoring

Employing single-cell RNA sequencing coupled with an array of bioinformatics approaches to ascertain the shared genetic characteristics between osteoporosis and obesity

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