Identification of diagnostic gene biomarkers related to immune infiltration in patients with idiopathic pulmonary fibrosis based on bioinformatics strategies

Abstract

ObjectiveThe study aims to identify potential diagnostic markers of idiopathic pulmonary fibrosis (IPF) and analyze the significance of immune cell infiltration in this pathology.Materials and methodsDownload two publicly available gene expression profiles (GSE10667 and GSE24206 datasets) from the GEO database including 48 Idiopathic pulmonary fibrosis (IPF) samples and 21 human control samples and select for distinctly expressed genes (DEG) from them. Lasso regression model and support vector machine recursive feature elimination S,V,R,F analysis were used to check candidate biomarkers. The area under the subject’s work characteristic curve (AUC) value is used to evaluate its recognition ability. The GSE53845 dataset (40 IPF patients and 8 controls) continue to validate the expression level and diagnostic value of biomarkers in IPF. Comprehensive analysis of immune infiltrated cells of IPF was performed using R software and immune cell infiltration estimation analysis tool- deconvolution algorithm (CIBERSORT).Results43 DEGs were identified in total. The identified DEGs mostly involve pneumonia, lung disease, collagen disease, obstructive pulmonary disease and other diseases. The activation of IL-17 signaling pathways, amoebic disease, interaction of viral proteins with cytokines and cytokine receptors, protein digestion and absorption, and flaccid hormone signaling pathways in IPF were different from the control group. The expression degree of CRTAC1, COL10A1, COMP, RPS4Y1, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 in IPF tissue were prominently higher than the normal group. Immune cell infiltration analysis showed that CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 were associated with monocytes, plasma cells, neutrophils, and regulatory (treg) T cells.ConclusionCRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 can be used as diagnostic markers for IPF, providing new ideas for the future study of IPF occurrence and molecular mechanisms.

Transcriptomics in idiopathic pulmonary fibrosis unveiled: a new perspective from differentially expressed genes to therapeutic targets

“…This methodology aligns with the research conducted by Dai et al. ( 15 ), but extends their work by providing further in-depth MR causal validation for the identified DEGs.…”

Commensal microbiome dysbiosis elicits IL-8 signaling to drive fibrotic skin disease

“…We found very distinct differences between them. Though IL-8 treated samples resulted in downregulated genes involved in fibrotic events, including extracellular matrix organizers ( COL4A1 , COL10A1 ) (37, 38), IL11 (39, 40). However, it can enhance canonical signaling drives fibrotic programs, as evidenced by upregulated TGFβR3 and SMAD3 that are essential in fibrosis (41, 42) (Supplemental Figure 6, D-F).…”

Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis

“…This model has made significant progress compared to other models in previous studies 37 . This progress is primarily reflected in the use of fewer feature variables, the validation of the model using a large-scale dataset, and its strong predictive performance.…”

“…Insulin-like growth factors (IGFs) and their binding proteins (IGFBPs) play a critical role in pulmonary fibrosis development and progression 37 . It has previously been shown that IGFBP2 and IGF-like family member 2 (IGFL2) are upregulated in SSc-PF and IPF 38 .…”