Novel kinase 1 regulates CD8+T cells as a potential therapeutic mechanism for idiopathic pulmonary fibrosis

Abstract

Idiopathic pulmonary fibrosis (IPF) is a rare, chronic and progressively worsening lung disease that poses a significant threat to patient prognosis, with a mortality rate exceeding that of some common malignancies. Effective methods for early diagnosis and treatment remain for this condition are elusive. In our study, we used the GEO database to access second-generation sequencing data and associated clinical information from IPF patients. By utilizing bioinformatics techniques, we identified crucial disease-related genes and their biological functions, and characterized their expression patterns. Furthermore, we mapped out the immune landscape of IPF, which revealed potential roles for novel kinase 1 and CD8+T cells in disease progression and outcome. These findings can aid the development of new strategies for the clinical diagnosis and treatment of IPF.

Keywords: CD8+T; Idiopathic pulmonary fibrosis; NUAK1; bioinformatics; immune landscape.

Figure 1

Data preprocessing. (A) A differential gene expression volcano plot is displayed using fold change and corrected P values. (B) A differential gene expression heatmap in which the different colors represent the expression trends in different tissues.

Figure 2

Functional enrichment analysis of DEGs. (A) KEGG enrichment analysis of the up-regulated DEGs, (B) KEGG enrichment analysis of down-regulated DEGs, (C) GO enrichment analysis of up-regulated DEGs, and (D) GO enrichment analysis of down-regulated DEGs. The larger the value, the smaller the FDR value. The size of the circles represents the number of enriched genes. The larger the circle size, the larger the number. In the enrichment results, p < 0.05 represents a statistically significant difference.

Figure 3

Construction of the WGCNA co-expression network. (A) Sample clustering dendrograms with tree leaves corresponding to the individual samples. (B) Soft threshold β = 6 and scale-free topological fit index (R2). (C) Clustered dendrograms were cut at a height of 0.25 in order to detect and combine all the similar modules. (D) Shows the original and combined modules obtained under the clustering tree. (E) A heatmap of module-trait correlations. (F) The turquoise module genes and DEGs when analyzed and displayed as a Venn diagram.

Figure 4

Hub gene selection. (A) Adjustment of feature selection in the least absolute shrinkage and selection operator model (LASSO). (B) The Random Forest error rate versus the number of classification trees and the top 20 DEGs found. (C) A LASSO- and Random Forest-generated Venn diagram of the genes screened.

Figure 5

Scatter plots of hub gene expression versus CD8+ T cells infiltration levels from three different datasets: (A-B) GSE28042. (C) GSE70866.

Figure 6

GSEA analysis of hub genes. (A) Expression levels of NUAK1 in controls and IPF patients. (B) GSEA analysis of NUAK1 expression.

Figure 7

PPI network and enrichment analysis of hub genes. (A) The PPI networks associated with NUAK1 expression. (B) GO analysis of genes associated with the PPI networks. (C) KEGG analysis of genes associated with the PPI networks.

Figure 8

Statistical analysis of pathological and immunohistochemical staining. A: HE staining (100x); B: Masson staining (100x); C: Immunohistochemical staining of NUAK1 using a rabbit polyclonal antibody to ARK5; D: Immunohistochemical staining of CD8 using a rabbit monoclonal antibody; Scale bar = 100µm E: Differences in staining of NUAK1 and CD8 between the different experimental groups. p < 0.0261 and 0.0012, respectively, were for the differences in the positive areas between the WT and model groups; n= 4 for the control group and n=5 for each of the other 3 groups.