Comprehensive Analysis of LncRNA AC010789.1 Delays Androgenic Alopecia Progression by Targeting MicroRNA-21 and the Wnt/β-Catenin Signaling Pathway in Hair Follicle Stem Cells

Xiong, Jiachao; Wu, Bingcheng; Hou, Qiang; Huang, Xiaofeng; Jia, Lingling; Li, Yufei; Jiang, Hua

doi:10.3389/fgene.2022.782750

Cited by 4 publications

(5 citation statements)

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“…AGA is a multifactorial disease, featured by typical clinical manifestations and high prevalence in both genders ( 26 ). To date, the essential factors implicated in the etiopathogenesis of AGA have been well characterized, represented by dihydrotestosterone (DHT) ( 27 , 28 ), genetic predisposition ( 29 , 30 ), environmental input ( 31 ), and aging ( 3 , 25 ). Minoxidil and finasteride remain the only two kinds of pharmacological therapies approved by the FDA ( 8 ), with encouraging efficacy presenting in the clinical settings ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

“…Androgenetic alopecia (AGA), also known as androgenic alopecia ( 1 ), is a type of non-scarring hair loss mainly characterized by a receding hairline on the forehead and sideburns, while the temporal and occipital areas remain unaffected, forming a characteristic “horseshoe” pattern ( 2 ), which is a intricated disorder with the interplay of genetic, mental, and environmental factors precipitating its onset ( 3 ). Intriguingly, the clinical manifestation of AGA is not identical in males and females, and the latter presents as top hair thinning and diffuse hair loss or a Christmas tree pattern ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

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Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

Zhou¹,

Zhongbo²,

Wang³

et al. 2022

Ann Transl Med

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Background: Androgenetic alopecia (AGA) is a type of non-scarring hair loss. Current drugs for AGA are accompanied by adverse reactions and a high recurrence rate. Thus, the discovery of diagnostic biomarkers and therapeutic targets for AGA remains imperatively warranted.Methods: The GSE90594 dataset, which contained scalp skin biopsies from 14 male AGA cases and healthy volunteers, was used to identify the differentially expressed genes (DEGs). Functional enrichment analysis was subsequently performed. Next, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database combined with the cytoHubba plugin of Cytoscape were used to obtain the key genes of AGA. Thereafter, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was performed to evaluate the relative abundance of immune cells between male AGA patients and healthy controls. The correlation between key genes and infiltrating immune cells was analyzed to obtain the significant immune-cell related genes (IRGs), then intersected with the DEGs between immortalized balding and non-balding human dermal papilla cells (DPCs) of the GSE93766 dataset as well as the DEGs obtained by the GSE90594 dataset, thus obtaining the hub genes of AGA. Finally, the hub genes were validated using GSE36169, which contained expression profiling of tissues biopsied from haired and bald scalps of five individuals with AGA.Results: A total of 234 DEGs were obtained from the GSE90594 dataset, which were mainly enriched in the extracellular matrix (ECM)-related pathways and immune-related activities. The STRING database and ten algorithms in the cytoHubba plugin of Cytoscape disclosed 21 key DEGs. The results of the CIBERSORT algorithm revealed the relative abundances of 20 kinds of immune cells between diseased and healthy individuals, and yielded 15 IRGs involved in the pathogenesis of AGA. Next, the intersection analysis identified four hub genes of AGA, comprising COL1A2, PCOLCE, ITGAX, and LOX. The GSE36169 dataset validated the expression pattern of hub genes in the haired scalp of AGA patients.Conclusions: We discovered that the hub genes identified are closely linked with the causative factors of AGA, which could be used as the viable diagnostic and therapeutic target in the clinical applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

Zhou¹,

Zhongbo²,

Wang³

et al. 2022

Ann Transl Med

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“…The gene expression datasets were collected from the GEO public database ( https://www.ncbi.nlm.nih.gov/geo ). 10 The AA-related datasets (GSE68801, GSE45512, and GSE80342) were download from GEO for further analysis. The AA-related datasets were based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), in which GSE68801 includes samples from 36 normal controls, 54 AAP patients, 9 AT patients, and 23 AU patients.…”

Section: Methodsmentioning

confidence: 99%

Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms

Xiong

Chen

Liu

et al. 2023

Precision Clinical Medicine

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Objectives Alopecia areata (AA) is an autoimmune related non cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe AA. However, there are limitations in early identification and intervention of AA patients who may progress to severe AA, which will help to improve the incidence rate and prognosis of severe AA. Methods We obtained two AA-related datasets from the GEO database, identified the differentially expressed genes (DEGs), and identified the module genes most related to severe AA through weighted gene co-expression network analysis. Functional enrichment analysis, construction of protein–protein interaction network and competing endogenous RNA network, and immune cell infiltration analysis were performed to clarify the underlying biological mechanisms of severe AA. Subsequently, pivotal immune monitoring genes (IMGs) were screened through multiple machines learning algorithms, and the diagnostic effectiveness of the pivotal IMGs were validated by ROC. Results A total of 150 severe AA-related DEGs were identified, and the upregulated DEGs were mainly enriched in immune response, while the downregulated DEGs were mainly enriched in pathways related to hair cycle and skin development. Following four IMGs (LGR5, SHISA2, HOXC13 and S100A3) with good diagnostic efficiency were obtained. As an important gene of hair follicle stem cells stemness, we verified in vivo that LGR5 downregulation may be an important link leading to severe AA. Conclusion Our findings provide comprehensive understanding of the pathogenesis and underlying biological processes in patients with AA, and identification four potential IMGs, which is helpful for the early diagnosis of severe AA.

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“…Gene expression profiles on melanoma cell lines and UV-irradiated mitochondria were downloaded from GEO database ( https://www.ncbi.nlm.nih.gov/geo ) ( Xiong et al, 2022 ). The melanoma cell lines datasets (GSE31909) and UV-irradiated mitochondria dataset (GSE3632) were download from GEO for further analysis.…”

Section: Methodsmentioning

confidence: 99%

Effect of Sun exposure-induced ferroptosis mechanisms on pathology and potential biological processes of primary melanoma by microarray data analysis

et al. 2022

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Objectives: Sunlight exposure is an important environmental factor in the pathogenesis of skin cutaneous melanoma (SKCM). Ultraviolet (UV) from sunlight can cause excessive intracellular production of reactive oxygen species (ROS), resulting in damage from oxidative stress to cells. As a major iron-rich and ROS-producing organelle, mitochondria are considered as an important place for cell ferroptosis. Thus, the pathology and potential biological process of UV exposure-induced ferroptosis in the development of SKCM has aroused our strong interest.Methods: Gene expression profile datasets of melanoma cell line datasets (GSE31909) and UV-irradiated mitochondria dataset (GSE3632) were downloaded from the Gene Expression Omnibus (GEO) database, and ferroptosis-related genes were obtained from the FerrDb v2 database. After identifying the common differentially expressed genes (DEGs), comprehensive analyzes were performed, including functional annotation, protein-protein interaction (PPI) network construction, hub gene identification, and gene and tissue protein expression levels, survival analysis, and immune cell infiltration analysis.Results: A total of 14 common DEGs was identified for subsequent analyses. Seven DEGs, including PSMB4, CRELD2, CDKN2A, TIMP1, NDRG1, ATF3 and JUND, have consistent performance in mRNA and protein expression in normal skin and SKCM tissues can be regarded as a good biomarker with SKCM diagnostic effectiveness. Functional enrichment analysis results indicate that HIF-1 signaling pathway and angiogenesis involved in the pathogenesis and development of SKCM. Induction of ferroptosis in tumor cells by enhancing the function of CD8+ T cells is expected to be an effective intervention to promote tumor therapy.Conclusion: Our study reveals the pathogenesis and potential biological processes of UV exposure-induced ferroptosis in the development of SKCM, which may provide potential immunotherapy targets for SKCM treatment via tumor cell ferroptosis mechanisms.

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Comprehensive Analysis of LncRNA AC010789.1 Delays Androgenic Alopecia Progression by Targeting MicroRNA-21 and the Wnt/β-Catenin Signaling Pathway in Hair Follicle Stem Cells

Cited by 4 publications

References 51 publications

Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms

Effect of Sun exposure-induced ferroptosis mechanisms on pathology and potential biological processes of primary melanoma by microarray data analysis

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