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Gene & Protein in Disease Drugs and immune infiltration in IPF

1. Introduction (CIBERSORT) were used to analyze immune infiltrations
in IPF samples. In addition, potential therapeutic drugs
Idiopathic pulmonary fibrosis (IPF) is a chronic targeting hub genes in IPF were predicted through the
progressive lung disease of unknown origin, which leads DrugBank database, Comparative Toxicogenomics
to fibrotic changes in the lung interstitium. It primarily Database (CTD), and Drug-Gene Interaction Database
affects older individuals and is confined to the lungs. (DGIdb). This comprehensive analysis aimed to identify
IPF is the most common type of idiopathic interstitial potential biomarkers or therapeutic targets for IPF.
pneumonia, substantially impacting patients’ quality of life
1
and ultimately leading to respiratory failure and death. 2. Methods
Research indicates that the incidence of IPF ranges from 14.0
to 42.7 cases/100,000 individuals. However, the influence 2.1. Gene expression dataset
2
of geographic, cultural, or racial factors on the occurrence Gene expression data for IPF were obtained from the gene
and prevalence of IPF remains unclear. IPF generally has expression omnibus (GEO) database, a publicly accessible
3
an unfavorable prognosis, with considerable variation in repository for gene expression datasets. A search was
its natural course and outcomes. If left untreated, patients performed using the keywords “idiopathic pulmonary
with IPF typically have a median survival of 2 – 3 years fibrosis” or “IPF,” organism “Homo sapiens,” entry type
post-diagnosis, with a 5-year survival rate of only 20%. 4 “Series,” and study type “Expression profiling by array,”
2
The etiological mechanisms of IPF are complex and yielding 69 microarray expression profile datasets related
not fully understood. Extensive research has revealed that to IPF. After careful examination, gene expression profiles
IPF pathogenesis involves changes in genetics, epigenetics, from three IPF tissue microarray datasets (GSE2052,
microRNA (miRNA) regulation, cell signaling pathways, GSE53845, and GSE110147) were collected. These datasets
apoptosis, and autophagy. miRNAs are small RNA were based on specific platforms: GPL1739 (Amersham
4
molecules that regulate gene expression and participate in Biosciences CodeLink Uniset Human I Bioarray), GPL6244
physiological processes such as tissue development, tissue ([HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array
repair, and cell proliferation. The miRNA regulatory [transcript (gene) version]), and GPL6480 (Agilent-014850
5,6
network in IPF has been extensively studied, highlighting Whole Human Genome Microarray 4×44K G4112F [Probe
7
its significant role in IPF pathogenesis. In addition, various Name version]). The data can be freely accessed online
immune cells, including macrophages, monocytes, T cells, through the GEO database. This study adhered to the
innate lymphoid cells, and neutrophils, play crucial roles in principles of the Declaration of Helsinki (revised in 2013),
IPF development. Therefore, examining specific changes with no involvement of human or animal experiments.
8,9
in immune cells in patients with IPF is highly valuable for
further research. 2.2. Analysis of RNA sequencing (RNA-seq) data and
identification of DEGs associated with IPF
Bioinformatics analysis of microarray data is widely
used to identify novel biomarkers and investigate their R software (version 4.2.2) was used to process RNA-
roles in various diseases. Weighted gene coexpression seq data and identify DEGs associated with IPF. The
network analysis (WGCNA) is a computational biology preprocessing steps included gene name and probe ID
tool that is used to construct gene coexpression networks, matching, handling of missing data, normalization, and
detect modules, and identify genes and modules of specific log2 transformation, which were performed using the
interest. WGCNA helps uncover potential biomarkers for LIMMA package (version 3.54.2) and impute package
different diseases. 10 (version 1.72.3). The LIMMA package was used to merge

This study determined the causes and mechanisms of three microarray expression profile datasets, and batch
IPF, focusing on miRNAs, target genes, and immune cells in effects and other variations were removed using the
the tissues of patients with IPF. Three IPF tissue microarray surrogate variable analysis package (version 3.46.0). IPF
datasets (GSE31821, GSE41177, and GSE79768) were DEGs were identified using LIMMA based on the criteria
integrated after removing batch differences. Differentially of a cutoff P < 0.05 and |log2 fold change (FC)| of >1. DEGs
expressed genes (DEGs) were selected based on the were visualized using the ggplot2 package (version 3.13)
intersection of module genes from WGCNA to identify and pheatmap package (version 1.0.12).
common genes (CGs) strongly associated with IPF. 2.3. Identification of IPF-associated gene modules
Functional annotation and protein–protein interaction using WGCNA
(PPI) analyses of CGs were conducted to identify hub
genes, and a miRNA–transcription factor (TF)–mRNA WGCNA is a systems biology approach used to identify
network was constructed. Bioinformatics methods key genes or hub genes within modules to investigate large-

Volume 3 Issue 4 (2024) 2 doi: 10.36922/gpd.4101