Identification and verification of EOMEs regulated network in Alopecia areata

Identification of SLC40A1, LCN2, CREB5, and SLC7A11 as ferroptosis-related biomarkers in alopecia areata through machine learning

Alopecia areata (AA) is a common non-scarring hair loss condition driven by the collapse of immune privilege and oxidative stress. The role of ferroptosis, a type of cell death linked to oxidative stress, in AA is yet to be explored, even though it's implicated in various diseases. Using transcriptome data from AA patients and controls from datasets GSE68801 and GSE80342, we aimed to identify AA diagnostic marker genes linked to ferroptosis. We employed Single-sample gene set enrichment analysis (ssGSEA) for immune cell infiltration evaluation. Correlations between ferroptosis-related differentially expressed genes (FRDEGs) and immune cells/functions were identified using Spearman analysis. Feature selection was done through Support vector machine-recursive feature elimination (SVM-RFE) and LASSO regression models. Validation was performed using the GSE80342 dataset, followed by hierarchical internal validation. We also constructed a nomogram to assess the predictive ability of FRDEGs in AA. Furthermore, the expression and distribution of these molecules were confirmed through immunofluorescence. Four genes, namely SLC40A1, LCN2, CREB5, and SLC7A11, were identified as markers for AA. A prediction model based on these genes showed high accuracy (AUC = 0.9052). Immunofluorescence revealed reduced expression of these molecules in AA patients compared to normal controls (NC), with SLC40A1 and CREB5 showing significant differences. Notably, they were primarily localized to the outer root sheath and in proximity to the sebaceous glands. Our study identified several ferroptosis-related genes associated with AA. These findings, emerging from the integration of immune cell infiltration analysis and machine learning, contribute to the evolving understanding of diagnostic and therapeutic strategies in AA. Importantly, this research lays a solid foundation for subsequent studies exploring the intricate relationship between AA and ferroptosis.

Hair Growth Effect of DN106212 in C57BL/6 Mouse and Its Network Pharmacological Mechanism of Action

Centipeda minima (CMX) has been widely investigated using network pharmacology and clinical studies for its effects on hair growth via the JAK/STAT signaling pathway. Human hair follicle papilla cells exhibit hair regrowth through the expression of Wnt signaling-related proteins. However, the mechanism of action of CMX in animals has not been elucidated fully. This study examined the effect of induced hair loss and its side-effects on the skin, and observed the mechanism of action of an alcoholic extract of CMX (DN106212) on C57BL/6 mice. Our results showed that DN106212 was more effective in promoting hair growth than dimethyl sulfoxide in the negative control and tofacitinib (TF) in the positive control when mice were treated with DN106212 for 16 days. We confirmed that DN106212 promotes the formation of mature hair follicles through hematoxylin and eosin staining. We also found that the expression of vascular endothelial growth factor (Vegfa), insulin-like growth factor 1 (Igf1), and transforming growth factor beta 1 (Tgfb1) is related to hair growth using PCR. DN106212-treated mice had significantly higher expression of Vegfa and Igf1 than TF-treated ones, and inhibiting the expression of Tgfb1 had similar effects as TF treatment. In conclusion, we propose that DN106212 increases the expression of hair growth factors, promotes the development of hair follicles, and promotes hair growth. Although additional experiments are needed, DN106212 may serve as an experimental basis for research on natural hair growth-promoting agents.

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

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.

CGB5, INHBA and TRAJ19 Hold Prognostic Potential as Immune Genes for Patients with Gastric Cancer

BACKGROUND

Gastric cancer (GC) seriously threatens people's health and life quality worldwide.

AIM

The current study sought to explore prognostic immune genes and their regulatory network in GC.

METHODS

First, expression data in GC and normal samples were analyzed based on bioinformatics analysis. Immune-related genes were identified and confirmed with univariate/multivariate Cox analysis and receiver-operating characteristic curve. The upstream transcription factors of immune genes were subsequently predicted, and their regulatory network was constructed. GC and adjacent normal tissues were obtained from 76 patients with GC to determine the expression patterns of immune genes and their correlation with overall prognosis. CD8⁺ T-cell infiltration of patients with high or low risk was detected by means of immunohistochemistry.

RESULTS

Bioinformatics analysis highlighted 3689 differentially expressed genes in GC, including 87 immune genes, 8 of which were significantly associated with patient survival. CGB5 and INHBA were high-risk genes, while TRAJ19 was identified as a low-risk gene, all of which were found to be regulated by 11 different transcription factors. Furthermore, CGB5 and INHBA exhibited negative correlation with the prognosis of GC patients; however, TRAJ19 was positively correlated with GC patient prognosis. The incidence of lymph node metastasis was higher, the pathological stage was advanced and the infiltrated CD8⁺ T cells were fewer in the high-risk GC group.

CONCLUSIONS

Overall, our findings identified the key roles of CGB5, INHBA and TRAJ19 in prognosis GC patients, serving as an important gene set for prognostic prediction.

Novel potential therapeutic targets of alopecia areata

Alopecia areata (AA) is a non-scarring hair loss disorder caused by autoimmunity. The immune collapse of the hair follicle, where interferon-gamma (IFN-γ) and CD8+ T cells accumulate, is a key factor in AA. However, the exact functional mechanism remains unclear. Therefore, AA treatment has poor efficacy maintenance and high relapse rate after drug withdrawal. Recent studies show that immune-related cells and molecules affect AA. These cells communicate through autocrine and paracrine signals. Various cytokines, chemokines and growth factors mediate this crosstalk. In addition, adipose-derived stem cells (ADSCs), gut microbiota, hair follicle melanocytes, non-coding RNAs and specific regulatory factors have crucial roles in intercellular communication without a clear cause, suggesting potential new targets for AA therapy. This review discusses the latest research on the possible pathogenesis and therapeutic targets of AA.

CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity

Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA-miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.

Translational impact of omics studies in alopecia areata: recent advances and future perspectives

INTRODUCTION

Alopecia areata (AA) is a non-scarring, hair loss disorder and a common autoimmune-mediated disease with an estimated lifetime risk of about 2%. To date, the treatment of AA is mainly based on suppression or stimulation of the immune response. Genomics and transcriptomics studies generated important insights into the underlying pathophysiology, enabled discovery of molecular disease signatures, which were used in some of the recent clinical trials to monitor drug response and substantiated the consideration of new therapeutic modalities for the treatment of AA such as abatacept, dupilumab, ustekinumab and Janus Kinase (JAK) inhibitors.

AREAS COVERED

In this review, genomics and transcriptomics studies in AA are discussed in detail with particular emphasis on their past and prospective translational impacts. Microbiome studies are also briefly introduced.

EXPERT OPINION

The generation of large datasets using the new high-throughput technologies has revolutionized medical research and AA has also benefited from the wave of omics studies. However, the limitations associated with JAK inhibitors and clinical heterogeneity in AA patients underscore the necessity for continuing omics research in AA for discovery of novel therapeutic modalities and development of clinical tools for precision medicine.

Resident human dermal γδT-cells operate as stress-sentinels: Lessons from the hair follicle

Murine γδT-cells have stress-surveillance functions and are implicated in autoimmunity. Yet, whether human γδT-cells are also stress sentinels and directly promote autoimmune responses in the skin is unknown. Using a novel (mini-)organ assay, we tested if human dermis resident γδT-cells can recognize stressed human scalp hair follicles (HFs) to promote an alopecia areata (AA)-like autoimmune response. Accordingly, we show that γδT-cells from healthy human scalp skin are activated (CD69⁺), up-regulate the expression of NKG2D and IFN-γ, and become cytotoxic when co-cultured with autologous stressed HFs ex vivo. These autologous γδT-cells induce HF immune privilege collapse, dystrophy, and premature catagen, i.e. three hallmarks of the human autoimmune HF disorder, AA. This is mediated by CXCL12, MICA, and in part by IFN-γ and CD1d. In conclusion, human dermal γδT-cells exert physiological stress-sentinel functions in human skin, where their excessive activity can promote autoimmunity towards stressed HFs that overexpress CD1d, CXCL12, and/or MICA.