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. 2023 May 22;6(2):pbad009.
doi: 10.1093/pcmedi/pbad009. eCollection 2023 Jun.

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

Jiachao Xiong  1   2 , Guodong Chen  3 , Zhixiao Liu  1 , Xuemei Wu  2 , Sha Xu  3 , Jun Xiong  1 , Shizhao Ji  4 , Minjuan Wu  1
Affiliations

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

Jiachao Xiong et al. Precis Clin Med. .

Abstract

Objectives: Alopecia areata (AA) is an autoimmune-related non-cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe forms of AA. However, there are limitations in early identification of AA, and intervention of AA patients who may progress to severe AA will help to improve the incidence rate and prognosis of severe AA.

Methods: We obtained two AA-related datasets from the gene expression omnibus 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 a 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 machine-learning algorithms, and the diagnostic effectiveness of the pivotal IMGs was validated by receiver operating characteristic.

Results: A total of 150 severe AA-related DEGs were identified; 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. 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 a comprehensive understanding of the pathogenesis and underlying biological processes in patients with AA, and identification of four potential IMGs, which is helpful for the early diagnosis of severe AA.

Keywords: alopecia areata; diagnosis; immune monitoring genes; immune response; machine learning.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
Functional correlation analysis of severe AA-related DEGs. (A) Venn diagram displays 31 upregulated and 119 downregulated severe AA-related DEGs. (B–D) GO analysis of the DEGs, including BP, CC, and MF, respectively. (E) KEGG pathway analysis of the DEGs. (F) Circle diagram shows the immune-related pathways, BPs, and related enriched genes. (F) Circle diagram shows the skin development related BPs and related enriched genes.
Figure 2.
Figure 2.
Construction of the ceRNA regulatory network and enrichment analysis. (A, B) Regulatory network of synergistically expressed mRNA–lncRNA. (C) The expression trend of DE-lncRNAs (RP11-315F22.1, RP11-25K19.1, and RP1-93H18.6) in the AAP group and severe AA group. (D–F) GO analysis of synergistic mRNAs, including BP, CC, and MF, respectively. (G) KEGG pathway analysis of the synergistic mRNAs. (H) The ceRNA network was constructed through Cytoscape. Pink and dark blue rhombuses represent upregulated and downregulated DE-lncRNAs, respectively. Red and green dots represent upregulated and downregulated AA-related DEGs, respectively. Purple and brown dots represent upregulated and downregulated severe AA-related DEGs, respectively. Orange triangle represents potentially regulated miRNAs. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Figure 3.
Figure 3.
Immune cell infiltration status in AA patients. (A) Histogram showed the composition of 22 kinds of immune cells in normal, AAP, AT, and AU samples. (B) Box diagram of the infiltration of immune cells between the four groups of samples. (C–F) Violin diagram shows the specific enrichment status of resting CD4 memory T cells, gamma delta T cells, resting mast cells, and neutrophils in the four groups. (G) Correlation heat map of 22 types of immune cells. (H) Correlation heat map between severe AA-related hub genes and immune cells in GSE68801; the top and bottom numbers represent correlation coefficients and P-values, respectively. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Figure 4.
Figure 4.
Machine learning in screening immune diagnostic biomarkers for severe AA and the diagnostic value evaluation. (A) Upset diagram shows the intersection of immune genes and severe AA-related genes. (B) Random forest algorithm shows the top 15 important genes, which are ranked based on the mean decrease accuracy. (C, D) Biomarker's screening in the LASSO regression model. The number of genes (n = 13) corresponding to the lowest point of the curve is the most suitable for severe AA diagnosis. (E, F) Trend line graph shows that STEM algorithm screens out the most upregulated and downregulated change genes, respectively. (G) Venn diagram shows that five potential immune monitoring genes are identified via the above three algorithms including random forest algorithm, LASSO regression algorithm, and STEM algorithm. (H) ROC curves of each immune monitoring gene (PRF1, LGR5, SHISA2, HOXC13 and S100A3) show the significant severe AA diagnostic value. AUC, area under the curve.
Figure 5.
Figure 5.
Construction of the AA model and section HE staining. (A) Brief schematic diagram shows the process of model establishment. (B) A model of AA at week 3 was successfully established and the area of hair removal in the mice was consistent with the area of drug intervention (n = 4). (C) HE staining of skin tissue sections in control group. (D) HE staining of skin tissue sections in model group. (E) HE staining revealed that the number of hair follicles in the model group was significantly less than that in the control group. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 6.
Figure 6.
The expression of LGR5 and CK19 were verified in vivo. (A) LGR5 immunohistochemistry staining of skin tissue sections in control group. (B) LGR5 immunohistochemistry staining of skin tissue sections in model group. (C) LGR5 immunohistochemistry staining revealed that LGR5-positive cells in the control group was significantly more than that in the model group. (D) CK19 immunohistochemistry staining of skin tissue sections in control group. (E) CK19 immunohistochemistry staining of skin tissue sections in model group. (F) CK19 immunohistochemistry staining revealed that CK19-positive cells in the control group was significantly more than that in the model group. *P < 0.05; **P < 0.01.
Figure 7.
Figure 7.
Schematic diagram showing the potential biological processes and early diagnosis of severe AA.
See this image and copyright information in PMC

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