Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disorder. Here, we performed a bioinformatics analysis using the GSE102660 dataset from the Gene Expression Omnibus database to identify differentially expressed circRNAs (DEcircRNAs) in tissues from IPF patients and healthy controls. The results identified 45 DEcircRNAs, among which expression of hsa_circ_0044226 was markedly higher in lung tissues from IPF patients than from healthy controls. Knocking down hsa_circ_0044226 expression using a targeted shRNA inhibited TGF-β1-induced fibrosis in RLE-6TN cells and in a bleomycin-induced mouse model of IPA. The diminished TGF-β1-induced fibrosis was associated with upregulated expression of E-cadherin and downregulated expression of α-SMA, collagen III and fibronectin 1, as well as with reduced expression of CDC27, suggesting inhibition of epithelial-to-mesenchymal transition (EMT). All of those effects were reversed by overexpression of CDC27. This suggests CDC27 overexpression abolishes the antifibrotic effect of hsa_circ_0044226 knockdown through activation of EMT. Furthermore, hsa_circ_0044226 knockdown decreased the expression of CDC27 in BLM-induced pulmonary fibrosis mouse model. Collectively then, these findings indicate that downregulation of hsa_circ_0044226 attenuates pulmonary fibrosis in vitro and in vivo by inhibiting CDC27, which in turn suppresses EMT. This suggests hsa_circ_0044226 may be a useful therapeutic target for the treatment of IPF.

The hypoxia-inducible factor-1α/endoplasmic reticulum stress signaling pathway (HIF-1α/ERS) has a crucial role in the pathogenetic mechanism of pulmonary fibrosis (PF). However, the upstream regulatory mediators of this pathway remain unclear. In the present study, by conducting bioinformatics analysis, we found that Krüppel-like factor 4 (KLF4) expression was decreased in the lung tissues of patients with idiopathic pulmonary fibrosis (IPF) as compared to that in patients with non-IPF. Furthermore, KLF4 expression was significantly reduced (p = 0.0331) in bleomycin-induced fibrotic HFL-1 cells. Moreover, in mice with bleomycin-induced PF, the degree of fibrosis was significantly reduced in mice overexpressing KLF4 as compared to that in wild-type mice. In mice and HFL-1 cells, KLF4 overexpression significantly reduced bleomycin-induced protein expression of HIF-1α (p = 0.0027) and ERS markers, particularly p-IRE1α (p = 0.0255) and ATF6 (p = 0.0002). By using the JASPAR database, we predicted that KLF4 has five binding sites for the HIF-1α promoter. The results of in vitro and in vivo studies suggest that KLF4 may inhibit PF through the HIF-1α/ERS pathway. This finding could guide the development of future therapies for PF and facilitate the identification of appropriate biomarkers for routine clinical diagnosis of PF.

PD1/PDL1 inhibitors have been adopted for the treatment of advanced non-small-cell lung cancer, and PDL1 expression has been investigated as a predictive biomarker for PD1/PDL1 inhibitor therapy. However, PDL1 lacks diagnostic accuracy in differentiating patients who are likely or unlikely to benefit. So, it is urgent and clinically significant to identify other associated predictive biomarkers for PD1/PDL1 inhibitor therapy. Our work was to identify PDL1-related biomarkers that could improve the patient selection for PD1/PDL1 inhibitor treatment. We obtained 500 genes coexpressed with PDL1 in lung adenocarcinoma from the TCGA database. Then, we identified 125 out of 500 genes differentially expressed in lung adenocarcinoma. A total of 39 genes were distinguished with prognostic value and associated with overall survival. Median survival time analysis based on gene expression level, protein-protein interaction analysis, GO and KEGG enrichment analyses, and significant GO and KEGG function consistency analyses were conducted to screen candidate biomarkers. Three candidate genes, BRCA1, BRIP1, and EREG, were identified to be functionally significantly coexpressed with PDL1. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response and cell activation. Additionally, to find potential adjuvant therapeutic targets in PD1/PDL1 inhibitor treatment, we performed transcription factor prediction analysis. A group of negative differential expression but PDL1-related biomarkers has been identified, which might help to assess the clinical management of lung cancer patients. A combination of potential biomarkers and adjuvant therapeutic targets with PDL1 will predict the response to PD1/PDL1 inhibitors more accurately and help with the patient selection for more personalized immune checkpoint inhibitor treatment.