IPF is a serious lung disease, and until today, there is no effective way to treat it. In this study, 71 FRDEGs from 293 FRGs were identified in disease samples compared to normal control in the GSE110147 dataset. The bioprocess enrichment analysis showed that the 71 FRDEGs mentioned above were significantly correlated with a series of biological processes: cellular responses to stimulus and various situations. Persistent alveolar epithelial injury and the abnormal repair are the important causes of lung fibrosis [25]. Therefore, cellular responses to the persistent injury are important in the development of IPF. Abnormal cellular responses may lead to epithelial-mesenchymal transition (EMT), which may promote the development of lung fibrosis [15]. Therefore, FRGs may participate in the development of IPF according to these biological processes.
Furthermore, KEGG pathways analysis of 71 FRDEGs and the module identified from the PPI network showed that cell growth and death, pathways associated cancer and signal transduction were significant enriched pathways. Similar to cancer, IPF affects susceptible individuals and shares common risk factors for cancer such as smoking, environmental or professional exposure, viral infections, and chronic tissue injury [26]. The incidence of cancer in IPF patients is higher compared with matched controls, especially for lung cancer [27]. Ferroptosis, FoxO signaling pathway, HIF-1 signaling pathway and so on play key roles in the development and prognosis of cancer [28–31]. In addition, the programmed death ligand-1/programmed cell death 1 (PD-L1/PD-1) axis can promote cancer cells to escape the surveillance of the immune system. And studies showed that PD-L1 was overexpressed in the lung tissues [32], lung fibroblasts [33] and CD4 T cells [34] in IPF. Therefore, we speculated that FRDEGs may participate in the development of cancer in patients with IPF according to these pathways.
MicroRNAs (miRNAs), a kind of small non-coding regulatory rna, are composed of 18–25 nucleotides that inhibit the translation or degradation of RNA transcripts in a sequence-specific manner, thus controlling the expression of protein-coding/non-protein-coding genes [35, 36]. To date, several studies have suggested that differently expressed miRNAs, DEGs, and microRNA-controlled differential gene expression represent key topics in the field of biomedical research into pulmonary fibrosis [37–39]. In this study, we constructed a miRNA-target FRDEGs network, and found that ITGB8 has the highest degree in the network, followed by ACSL4 and PIK3CA, which may be important biomarkers for regulating IPF. According to searching in the ILDGDB database [40] (a manually curated database of genomics, transcriptomics, proteomics and drug information for interstitial lung diseases), no related study was found for the three genes in patients with IPF. However, studies have verified the important role of ITGB8 in renal fibrosis [41], ACSL4 in liver fibrosis [42] and PIK3CA in myocardial fibrosis [43]. Therefore, further study is needed.
Subsequently, we verified the expression of 19 candidate key genes derived from the miRNA-target network and the PPI network in the GSE32537 dataset, then, 5 key genes were found. According to linear regression, ACSL1 was the strongest predictor for lung function and quality of life. ACSL1 plays a key role in fatty acid metabolism. Studies have found that lipid metabolism dysregulation play an important role in the pathogenesis of IPF [44, 45]. In addition, the levels of stearic acid (the one of fatty acid) is down-regulated in IPF lung tissues than in control lung tissues, and further study found that stearic acid had antifibrotic activity [45]. Therefore, ACSL1 may play a key role in the development of IPF according to regulating the fatty acid metabolism. Interestingly, ACSL1 is up-regulated in the GSE110147 dataset, however, it is down-regulated in the GSE32537 dataset. The expression level of ACSL1 may need further study to confirm.
The drugs were also screened in DrugBank and PubMed for ACSL1, ITGB8 and CP. Four drugs and sixteen drugs have been found to act on ACSL1 and CP, respectively. For example, representative compound 13 was remarkable inhibitor against not only ACSL1 (IC50 = 0.042 µM) but also other ACSL isoforms [23]. However, more experimental verifications are still needed to prove this hypothesis.