Stroke is one of the leading causes of death and disability worldwide. Evidence shows that ischemic stroke (IS) accounts for nearly 80 percent of all strokes and that the etiology, risk factors, and prognosis of this disease differ by gender. Female patients may bear a greater burden than male patients. The immune system may play an important role in the pathophysiology of females with IS. Therefore, it is critical to investigate the key biomarkers and immune infiltration of female IS patients to develop effective treatment methods. Herein, we used weighted gene co-expression network analysis (WGCNA) to determine the key modules and core genes in female IS patients using the GSE22255, GSE37587, and GSE16561 datasets from the GEO database. Subsequently, we performed functional enrichment analysis and built a protein-protein interaction (PPI) network. Ten genes were selected as the true central genes for further investigation. After that, we explored the specific molecular and biological functions of these hub genes to gain a better understanding of the underlying pathogenesis of female IS patients. Moreover, the “Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” was used to examine the distribution pattern of immune subtypes in female patients with IS and normal controls, revealing a new potential target for clinical treatment of the disease.

Stroke is one of the leading causes of patients' death and long‐term disability worldwide, and ischaemic stroke (IS) accounts for nearly 80% of all strokes. Differential genes and weighted gene co‐expression network analysis (WGCNA) in male and female patients with IS were compared. The authors used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) to analyse the distribution pattern of immune subtypes between male and female patients. In this study, 141 up‐regulated and 61 down‐regulated genes were gathered and distributed into five modules in response to their correlation degree to clinical traits. The criterion for Gene Ontology (GO) term and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway indicated that detailed analysis had the potential to enhance clinical prediction and to identify the gender‐related mechanism. After that, the expression levels of hub genes were measured via the quantitative real‐time PCR (qRT‐PCR) method. Finally, CCL20, ICAM1 and PTGS2 were identified and these may be some promising targets for sex differences in IS. Besides, the hub genes were further verified by rat experiments. Furthermore, these CIBERSORT results showed that T cells CD8 and Monocytes may be the target for the treatment of male and female patients, respectively.

Background: Asthma ranks among the most prevalent non-communicable diseases worldwide. Previous studies have elucidated the significant role of the immune system in its pathophysiology. Nevertheless, the immune-related mechanisms underlying asthma are complex and still inadequately understood. Thus, our objective was to investigate novel key biomarkers and immune infiltration characteristics associated with asthma by employing integrated bioinformatics tools. Methods: In this study, we conducted a weighted gene co-expression network analysis (WGCNA) to identify key modules and genes potentially implicated in asthma. Functional annotation of these key modules and genes was carried out through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Additionally, we constructed a protein–protein interaction (PPI) network using the STRING database to identify 10 hub genes. Furthermore, we evaluated the relative proportion of immune cells in bronchial epithelial cell samples from 20 healthy individuals and 88 asthmatic patients using CIBERSORT. Finally, we validated the hub genes and explored their correlation with immune infiltration. Results: Furthermore, 20 gene expression modules and 10 hub genes were identified herein. Among them, complement component 3 (C3), prostaglandin I2 receptor (PTGIR), parathyroid hormone-like hormone (PTHLH), and C-X3-C motif chemokine ligand 1 (CX3CL1) were closely correlated with the infiltration of immune cells. They may be novel candidate biomarkers or therapeutic targets for asthma. Furthermore, B cells memory, and plasma cells might play an important role in immune cell infiltration after asthma. Conclusions: C3, PTGIR, CX3CL1, and PTHLH have important clinical diagnostic values and are correlated with infiltration of multiple immune cell types in asthma. These hub genes, B cells memory, and plasma cells may become important biological targets for therapeutic asthma drug screening and drug design.